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Fault Modeling and Testing of Spiking Neural Network Chips
编号：48 访问权限：公开 更新：2021-08-19 20:37:15 浏览：257次 口头报告

报告开始：2021年08月19日 20:40（Asia/Shanghai）

报告时间：20min

所在会场：[RS] Regular Paper Session [RS1] A1. When Machine Learning Meets Testing and Security

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摘要
Spiking neural network (SNN) is a very promising low-power neural network that can be implemented in asynchronous circuits.  However, it is hard to test SNN chips since they are inherently probabilistic and fault tolerant.  So far, there is no good fault model and test methodology suitable for SNN chips.  In this paper, we propose seven behavior fault models for SNN based on the function of neurons and synapses.  We also propose a test methodology, which considers the output response as a distribution rather than specific values.  The experiment results on a MNIST dataset show that although SNN is fault tolerant, two fault models are still critical for SNN chips.  Given the digit recognition application, the accuracy of chips that passed our test is 88.90%, which is indistinguishable from that of good chips, even in the effects of random seeds.
 
关键词
Spiking neural network;Asynchronous circuits;Fault model;Fault simulation;test methodology
报告人
I-Wei Chiu
National Taiwan University

I-Wei Chiu received his BSEE degree from National Taiwan Normal University, Taipei, Taiwan, in 2020, where he currently is working toward the MSEE degree in electrical engineering. His research interests include neuromorphic circuits testing.

Li James Chien Mo
National Taiwan University

James Chien-Mo Li received his BSEE degree in 1993 from National Taiwan University, Taipei, Taiwan.  He received his MSEE and PhD degrees in electrical engineering from Stanford University in 1997 and 2002 respectively. He is currently a professor of Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan.  His research interest includes test generation, low power testing, flexible electronics, and diagnosis. He is a member of the IEEE.

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重要日期

  • 会议日期

    08月18日

    2021

    至

    08月20日

    2021

  • 05月10日 2021

    初稿截稿日期

  • 08月16日 2021

    提前注册日期

  • 08月19日 2021

    报告提交截止日期

  • 08月20日 2021

    注册截止日期

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